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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Comparing non-destructive 3D X-ray computed tomography with destructive optical microscopy for microstructural characterization of fiber reinforced composites
Due to their lightweight, relatively high stiffness properties, and formability into complex shapes, discontinuous fiber composites are advantageous for producing small and medium size components. Improved characterization techniques and post-processing methodologies are required for more reliable quantification of the microstructure and defect distributions in these materials, in order to employ model-based approaches to assess their structural integrity. This work compares a non-destructive X-ray approach with a destructive optical microscopy approach for characterizing the microstructural attributes, specifically the fiber volume fraction, porosity volume fraction, fiber orientation distribution, and fiber length distribution of discontinuous glass fibers in a polypropylene matrix. Additionally, a method for destructively determining the ambiguous components of the orientation tensor (related to the sign ambiguity of the out-of-plane angle in a destructive cross-sectional cut of a fiber) over a large surface area is included. It was found that fiber volume fraction and average fiber aspect ratio matched well, while fiber orientation and porosity detection had small but notable differences. The differences in the detection capabilities of each technique are quantified and discussed shedding light on the specific advantages and disadvantages of each approach, and enabling engineers to quantify uncertainty in their microstructural characterization measurements especially as they relate to model based structural integrity activities.
Life cycle assessment and cost analysis of hybrid fiber-reinforced engine beauty cover in comparison with glass fiber-reinforced counterpart
Life cycle assessment is a useful tool that helps to quantify the ecological impact of a product. It also enables us to compare two products. Regardless of its weaknesses, this tool is by far one of the best methods introduced and is one of the most complicated techniques available for environmental assessment. While the benefit of using bio-based materials instead of synthetic materials is well known, to date very few studies are available comparing the two products. The aim of this paper is to compare a currently available car engine beauty cover with a hybrid bio-based cover. This study's results show that the new hybrid materials not only perform better in terms of emissions during car operation (because of the fuel savings resulting from lightweighting), but that their production and end of life is also environmentally benign. A cost analysis of the two types of engine covers shows that the new hybrid materials are a good substitute for current materials because their manufacture costs half that of current materials. - Highlights: •Hybrid composite surpasses glass fiber composite although it has 10% carbon fiber. •The cost of producing the part out of hybrid material is lower than glass fiber. •The natural fibers contained composite had a better performance on eutrophication. •Hybrid composite outperforms in all the impact categories but the water and wood.
Reinforced concrete and fiber reinforced concrete panels subjected to blast detonations and post-blast static tests
Not Available
Stable lithium electrodeposition in salt-reinforced electrolytes
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Phase stability of SiC/SiC fiber reinforced composites: The effect of processing on the formation of α and β phases
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Supramolecular elastomers. Particulate β-sheet nanocrystal-reinforced synthetic elastic networks
Not Available
Direct observation of toughening and R-curve behavior in carbon nanotube reinforced silicon nitride
Not provided.
Strong reinforcement effects in 2D cellulose nanofibril-graphene oxide (CNF-GO) nanocomposites due to GO-induced CNF ordering
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Impacts of Mathematical Optimizations on Reinforcement Learning Policy Performance.
Abstract not provided.